using DocumentCreationSystem.Models;
using Microsoft.Extensions.Logging;
using Microsoft.Extensions.Configuration;
using System.Drawing;
using System.Text.Json;
using System.IO;

namespace DocumentCreationSystem.Services
{
    /// <summary>
    /// 多模态内容处理器 - 支持图像、音频、视频等多媒体内容的AI处理
    /// </summary>
    public interface IMultiModalContentProcessor
    {
        Task<ImageAnalysisResult> AnalyzeImageAsync(string imagePath, ImageAnalysisOptions options);
        Task<string> GenerateImageDescriptionAsync(string imagePath, string style = "detailed");
        Task<string> ExtractTextFromImageAsync(string imagePath);
        Task<AudioTranscriptionResult> TranscribeAudioAsync(string audioPath, string language = "zh");
        Task<VideoAnalysisResult> AnalyzeVideoAsync(string videoPath, VideoAnalysisOptions options);
        Task<string> GenerateImageFromTextAsync(string description, ImageGenerationOptions options);
        Task<MultiModalCreationResult> CreateMultiModalContentAsync(MultiModalCreationRequest request);
        Task<ContentSummaryResult> SummarizeMultiModalContentAsync(List<string> contentPaths);
    }

    public class MultiModalContentProcessor : IMultiModalContentProcessor
    {
        private readonly ILogger<MultiModalContentProcessor> _logger;
        private readonly IAIService _aiService;
        private readonly IConfiguration _configuration;

        public MultiModalContentProcessor(
            ILogger<MultiModalContentProcessor> logger,
            IAIService aiService,
            IConfiguration configuration)
        {
            _logger = logger;
            _aiService = aiService;
            _configuration = configuration;
        }

        /// <summary>
        /// 分析图像内容
        /// </summary>
        public async Task<ImageAnalysisResult> AnalyzeImageAsync(string imagePath, ImageAnalysisOptions options)
        {
            try
            {
                _logger.LogInformation($"开始分析图像: {imagePath}");

                if (!File.Exists(imagePath))
                {
                    throw new FileNotFoundException($"图像文件不存在: {imagePath}");
                }

                var result = new ImageAnalysisResult
                {
                    ImagePath = imagePath,
                    AnalyzedAt = DateTime.Now
                };

                // 基础图像信息
                result.BasicInfo = await ExtractBasicImageInfo(imagePath);

                // AI视觉分析
                if (options.EnableAIAnalysis)
                {
                    result.AIAnalysis = await PerformAIImageAnalysis(imagePath, options);
                }

                // 对象检测
                if (options.EnableObjectDetection)
                {
                    result.DetectedObjects = await DetectObjectsInImage(imagePath);
                }

                // 文本提取
                if (options.EnableTextExtraction)
                {
                    result.ExtractedText = await ExtractTextFromImageAsync(imagePath);
                }

                // 情感分析
                if (options.EnableEmotionAnalysis)
                {
                    result.EmotionAnalysis = await AnalyzeImageEmotion(imagePath);
                }

                _logger.LogInformation($"图像分析完成: {imagePath}");
                return result;
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, $"图像分析失败: {imagePath}");
                throw;
            }
        }

        /// <summary>
        /// 生成图像描述
        /// </summary>
        public async Task<string> GenerateImageDescriptionAsync(string imagePath, string style = "detailed")
        {
            var prompt = style.ToLower() switch
            {
                "simple" => "请用简洁的语言描述这张图片的主要内容。",
                "detailed" => "请详细描述这张图片，包括场景、人物、物体、颜色、构图等各个方面。",
                "creative" => "请用富有创意和想象力的语言描述这张图片，可以加入一些文学性的表达。",
                "technical" => "请从技术角度分析这张图片，包括拍摄技巧、光线、构图等专业要素。",
                _ => "请描述这张图片的内容。"
            };

            // 注意：这里需要支持视觉模型的AI服务
            // 当前实现为模拟，实际需要集成支持图像输入的AI模型
            return await _aiService.GenerateTextAsync($"{prompt}\n\n[图像路径: {imagePath}]", 500, 0.7f);
        }

        /// <summary>
        /// 从图像中提取文本 (OCR)
        /// </summary>
        public async Task<string> ExtractTextFromImageAsync(string imagePath)
        {
            try
            {
                // 这里应该集成OCR服务，如Tesseract、百度OCR、腾讯OCR等
                // 当前为模拟实现
                _logger.LogInformation($"开始OCR文本提取: {imagePath}");

                // 模拟OCR处理
                await Task.Delay(1000);

                // 实际实现应该调用OCR API
                var extractedText = await SimulateOCRExtraction(imagePath);

                _logger.LogInformation($"OCR文本提取完成，提取到 {extractedText.Length} 个字符");
                return extractedText;
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, $"OCR文本提取失败: {imagePath}");
                return string.Empty;
            }
        }

        /// <summary>
        /// 转录音频内容
        /// </summary>
        public async Task<AudioTranscriptionResult> TranscribeAudioAsync(string audioPath, string language = "zh")
        {
            try
            {
                _logger.LogInformation($"开始音频转录: {audioPath}");

                if (!File.Exists(audioPath))
                {
                    throw new FileNotFoundException($"音频文件不存在: {audioPath}");
                }

                var result = new AudioTranscriptionResult
                {
                    AudioPath = audioPath,
                    Language = language,
                    TranscribedAt = DateTime.Now
                };

                // 音频基础信息
                result.AudioInfo = await ExtractAudioInfo(audioPath);

                // 语音转文本
                result.TranscriptionText = await PerformSpeechToText(audioPath, language);

                // 说话人识别
                result.SpeakerSegments = await IdentifySpeakers(audioPath);

                // 情感分析
                result.EmotionAnalysis = await AnalyzeAudioEmotion(audioPath);

                // 关键词提取
                result.Keywords = await ExtractAudioKeywords(result.TranscriptionText);

                _logger.LogInformation($"音频转录完成: {audioPath}");
                return result;
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, $"音频转录失败: {audioPath}");
                throw;
            }
        }

        /// <summary>
        /// 分析视频内容
        /// </summary>
        public async Task<VideoAnalysisResult> AnalyzeVideoAsync(string videoPath, VideoAnalysisOptions options)
        {
            try
            {
                _logger.LogInformation($"开始视频分析: {videoPath}");

                if (!File.Exists(videoPath))
                {
                    throw new FileNotFoundException($"视频文件不存在: {videoPath}");
                }

                var result = new VideoAnalysisResult
                {
                    VideoPath = videoPath,
                    AnalyzedAt = DateTime.Now
                };

                // 视频基础信息
                result.VideoInfo = await ExtractVideoInfo(videoPath);

                // 关键帧提取
                if (options.EnableKeyFrameExtraction)
                {
                    result.KeyFrames = await ExtractKeyFrames(videoPath, options.KeyFrameInterval);
                }

                // 音频转录
                if (options.EnableAudioTranscription)
                {
                    var audioPath = await ExtractAudioFromVideo(videoPath);
                    result.AudioTranscription = await TranscribeAudioAsync(audioPath);
                }

                // 场景检测
                if (options.EnableSceneDetection)
                {
                    result.Scenes = await DetectVideoScenes(videoPath);
                }

                // 对象跟踪
                if (options.EnableObjectTracking)
                {
                    result.TrackedObjects = await TrackObjectsInVideo(videoPath);
                }

                _logger.LogInformation($"视频分析完成: {videoPath}");
                return result;
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, $"视频分析失败: {videoPath}");
                throw;
            }
        }

        /// <summary>
        /// 根据文本生成图像
        /// </summary>
        public async Task<string> GenerateImageFromTextAsync(string description, ImageGenerationOptions options)
        {
            try
            {
                _logger.LogInformation($"开始根据文本生成图像: {description}");

                // 这里应该集成图像生成AI服务，如DALL-E、Midjourney、Stable Diffusion等
                // 当前为模拟实现
                var generatedImagePath = await SimulateImageGeneration(description, options);

                _logger.LogInformation($"图像生成完成: {generatedImagePath}");
                return generatedImagePath;
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, $"图像生成失败: {description}");
                throw;
            }
        }

        /// <summary>
        /// 创建多模态内容
        /// </summary>
        public async Task<MultiModalCreationResult> CreateMultiModalContentAsync(MultiModalCreationRequest request)
        {
            try
            {
                _logger.LogInformation($"开始创建多模态内容: {request.Title}");

                var result = new MultiModalCreationResult
                {
                    Title = request.Title,
                    CreatedAt = DateTime.Now
                };

                // 文本内容生成
                if (request.IncludeText)
                {
                    result.TextContent = await GenerateTextContent(request);
                }

                // 图像生成
                if (request.IncludeImages)
                {
                    result.GeneratedImages = await GenerateImages(request);
                }

                // 音频生成
                if (request.IncludeAudio)
                {
                    result.GeneratedAudio = await GenerateAudio(request);
                }

                // 视频生成
                if (request.IncludeVideo)
                {
                    result.GeneratedVideo = await GenerateVideo(request);
                }

                // 内容整合
                result.IntegratedContent = await IntegrateMultiModalContent(result);

                _logger.LogInformation($"多模态内容创建完成: {request.Title}");
                return result;
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, $"多模态内容创建失败: {request.Title}");
                throw;
            }
        }

        /// <summary>
        /// 总结多模态内容
        /// </summary>
        public async Task<ContentSummaryResult> SummarizeMultiModalContentAsync(List<string> contentPaths)
        {
            try
            {
                _logger.LogInformation($"开始总结多模态内容，共 {contentPaths.Count} 个文件");

                var result = new ContentSummaryResult
                {
                    ContentPaths = contentPaths,
                    SummarizedAt = DateTime.Now
                };

                var allContent = new List<string>();

                foreach (var path in contentPaths)
                {
                    var extension = Path.GetExtension(path).ToLower();
                    string content = extension switch
                    {
                        ".jpg" or ".jpeg" or ".png" or ".bmp" => await GenerateImageDescriptionAsync(path),
                        ".mp3" or ".wav" or ".m4a" => (await TranscribeAudioAsync(path)).TranscriptionText,
                        ".mp4" or ".avi" or ".mov" => (await AnalyzeVideoAsync(path, new VideoAnalysisOptions())).Summary,
                        ".txt" or ".md" or ".docx" => await File.ReadAllTextAsync(path),
                        _ => $"未支持的文件类型: {extension}"
                    };

                    allContent.Add($"文件 {Path.GetFileName(path)}:\n{content}");
                }

                // 生成综合摘要
                var combinedContent = string.Join("\n\n", allContent);
                var summaryPrompt = $"请对以下多模态内容进行综合总结:\n\n{combinedContent}\n\n总结:";
                
                result.Summary = await _aiService.GenerateTextAsync(summaryPrompt, 1000, 0.5f);

                // 提取关键主题
                result.KeyThemes = await ExtractKeyThemes(combinedContent);

                // 生成标签
                result.Tags = await GenerateContentTags(combinedContent);

                _logger.LogInformation($"多模态内容总结完成");
                return result;
            }
            catch (Exception ex)
            {
                _logger.LogError(ex, $"多模态内容总结失败");
                throw;
            }
        }

        #region 私有辅助方法

        private async Task<ImageBasicInfo> ExtractBasicImageInfo(string imagePath)
        {
            // 提取图像基础信息：尺寸、格式、大小等
            var fileInfo = new FileInfo(imagePath);
            
            // 这里应该使用图像处理库获取详细信息
            return new ImageBasicInfo
            {
                FileName = fileInfo.Name,
                FileSize = fileInfo.Length,
                Format = Path.GetExtension(imagePath),
                CreatedAt = fileInfo.CreationTime,
                // Width, Height 等需要通过图像库获取
            };
        }

        private async Task<string> PerformAIImageAnalysis(string imagePath, ImageAnalysisOptions options)
        {
            // 这里应该调用支持视觉的AI模型
            var prompt = "请分析这张图片的内容，包括主要对象、场景、颜色、构图等。";
            return await _aiService.GenerateTextAsync($"{prompt}\n[图像: {imagePath}]", 800, 0.7f);
        }

        private async Task<List<DetectedObject>> DetectObjectsInImage(string imagePath)
        {
            // 对象检测实现
            await Task.Delay(500); // 模拟处理时间
            return new List<DetectedObject>
            {
                new DetectedObject { Name = "示例对象", Confidence = 0.95f, BoundingBox = "0,0,100,100" }
            };
        }

        private async Task<EmotionAnalysisResult> AnalyzeImageEmotion(string imagePath)
        {
            // 图像情感分析
            await Task.Delay(300);
            return new EmotionAnalysisResult
            {
                PrimaryEmotion = "平静",
                Confidence = 0.8f,
                EmotionScores = new Dictionary<string, float>
                {
                    ["快乐"] = 0.3f,
                    ["悲伤"] = 0.1f,
                    ["平静"] = 0.8f,
                    ["愤怒"] = 0.05f
                }
            };
        }

        private async Task<string> SimulateOCRExtraction(string imagePath)
        {
            // 模拟OCR提取
            await Task.Delay(800);
            return "这是从图像中提取的示例文本内容。";
        }

        private async Task<AudioInfo> ExtractAudioInfo(string audioPath)
        {
            var fileInfo = new FileInfo(audioPath);
            return new AudioInfo
            {
                FileName = fileInfo.Name,
                FileSize = fileInfo.Length,
                Format = Path.GetExtension(audioPath),
                Duration = TimeSpan.FromMinutes(5) // 应该通过音频库获取实际时长
            };
        }

        private async Task<string> PerformSpeechToText(string audioPath, string language)
        {
            // 语音转文本实现
            await Task.Delay(2000); // 模拟处理时间
            return "这是从音频中转录的示例文本内容。";
        }

        private async Task<List<SpeakerSegment>> IdentifySpeakers(string audioPath)
        {
            await Task.Delay(1000);
            return new List<SpeakerSegment>
            {
                new SpeakerSegment { SpeakerId = "Speaker1", StartTime = TimeSpan.Zero, EndTime = TimeSpan.FromMinutes(2) }
            };
        }

        private async Task<EmotionAnalysisResult> AnalyzeAudioEmotion(string audioPath)
        {
            await Task.Delay(500);
            return new EmotionAnalysisResult
            {
                PrimaryEmotion = "中性",
                Confidence = 0.7f
            };
        }

        private async Task<List<string>> ExtractAudioKeywords(string transcriptionText)
        {
            var prompt = $"请从以下文本中提取关键词:\n{transcriptionText}\n关键词:";
            var result = await _aiService.GenerateTextAsync(prompt, 200, 0.3f);
            return result.Split(',').Select(k => k.Trim()).ToList();
        }

        private async Task<VideoInfo> ExtractVideoInfo(string videoPath)
        {
            var fileInfo = new FileInfo(videoPath);
            return new VideoInfo
            {
                FileName = fileInfo.Name,
                FileSize = fileInfo.Length,
                Format = Path.GetExtension(videoPath),
                Duration = TimeSpan.FromMinutes(10) // 应该通过视频库获取
            };
        }

        private async Task<List<KeyFrame>> ExtractKeyFrames(string videoPath, TimeSpan interval)
        {
            await Task.Delay(3000);
            return new List<KeyFrame>
            {
                new KeyFrame { Timestamp = TimeSpan.Zero, ImagePath = "frame_001.jpg" }
            };
        }

        private async Task<string> ExtractAudioFromVideo(string videoPath)
        {
            // 从视频中提取音频
            await Task.Delay(1000);
            return videoPath.Replace(".mp4", "_audio.wav");
        }

        private async Task<List<VideoScene>> DetectVideoScenes(string videoPath)
        {
            await Task.Delay(2000);
            return new List<VideoScene>
            {
                new VideoScene { StartTime = TimeSpan.Zero, EndTime = TimeSpan.FromMinutes(2), Description = "开场场景" }
            };
        }

        private async Task<List<TrackedObject>> TrackObjectsInVideo(string videoPath)
        {
            await Task.Delay(4000);
            return new List<TrackedObject>
            {
                new TrackedObject { ObjectId = "obj1", ObjectType = "人物", TrackingData = "tracking_data" }
            };
        }

        private async Task<string> SimulateImageGeneration(string description, ImageGenerationOptions options)
        {
            await Task.Delay(5000); // 模拟图像生成时间
            var fileName = $"generated_{DateTime.Now:yyyyMMdd_HHmmss}.png";
            var outputPath = Path.Combine(options.OutputDirectory ?? "generated_images", fileName);
            
            // 这里应该调用实际的图像生成API
            return outputPath;
        }

        private async Task<string> GenerateTextContent(MultiModalCreationRequest request)
        {
            var prompt = $"根据以下要求创建文本内容:\n主题: {request.Title}\n风格: {request.Style}\n长度: {request.TargetLength}\n\n内容:";
            return await _aiService.GenerateTextAsync(prompt, request.TargetLength, 0.8f);
        }

        private async Task<List<string>> GenerateImages(MultiModalCreationRequest request)
        {
            var images = new List<string>();
            for (int i = 0; i < request.ImageCount; i++)
            {
                var imagePrompt = $"{request.Title} - 图像 {i + 1}";
                var imagePath = await GenerateImageFromTextAsync(imagePrompt, new ImageGenerationOptions());
                images.Add(imagePath);
            }
            return images;
        }

        private async Task<List<string>> GenerateAudio(MultiModalCreationRequest request)
        {
            // 音频生成实现
            await Task.Delay(3000);
            return new List<string> { "generated_audio.mp3" };
        }

        private async Task<string> GenerateVideo(MultiModalCreationRequest request)
        {
            // 视频生成实现
            await Task.Delay(10000);
            return "generated_video.mp4";
        }

        private async Task<string> IntegrateMultiModalContent(MultiModalCreationResult result)
        {
            // 整合多模态内容
            await Task.Delay(1000);
            return "integrated_content.html";
        }

        private async Task<List<string>> ExtractKeyThemes(string content)
        {
            var prompt = $"请从以下内容中提取主要主题:\n{content}\n主题:";
            var result = await _aiService.GenerateTextAsync(prompt, 300, 0.3f);
            return result.Split(',').Select(t => t.Trim()).ToList();
        }

        private async Task<List<string>> GenerateContentTags(string content)
        {
            var prompt = $"请为以下内容生成标签:\n{content}\n标签:";
            var result = await _aiService.GenerateTextAsync(prompt, 200, 0.3f);
            return result.Split(',').Select(t => t.Trim()).ToList();
        }

        #endregion
    }
}
